Overview

Dataset statistics

Number of variables19
Number of observations11724
Missing cells0
Missing cells (%)0.0%
Duplicate rows57
Duplicate rows (%)0.5%
Total size in memory1.7 MiB
Average record size in memory152.0 B

Variable types

Categorical8
Numeric11

Alerts

Dataset has 57 (0.5%) duplicate rowsDuplicates
is_furnished is highly overall correlated with is_temporaryHigh correlation
is_temporary is highly overall correlated with is_furnishedHigh correlation
latitude is highly overall correlated with zipcodeHigh correlation
livingspace is highly overall correlated with number_of_roomsHigh correlation
longitude is highly overall correlated with zipcodeHigh correlation
number_of_rooms is highly overall correlated with livingspaceHigh correlation
price_display_type is highly overall correlated with price_unitHigh correlation
price_unit is highly overall correlated with price_display_typeHigh correlation
zipcode is highly overall correlated with latitude and 1 other fieldsHigh correlation
object_category is highly imbalanced (81.5%)Imbalance
price_display_type is highly imbalanced (99.8%)Imbalance
price_unit is highly imbalanced (99.8%)Imbalance
is_temporary is highly imbalanced (62.5%)Imbalance
is_selling_furniture is highly imbalanced (81.7%)Imbalance
reserved is highly imbalanced (99.3%)Imbalance
object_type has 9318 (79.5%) zerosZeros
number_of_rooms has 215 (1.8%) zerosZeros
floor has 2812 (24.0%) zerosZeros
year_built has 294 (2.5%) zerosZeros
year_renovated has 757 (6.5%) zerosZeros
livingspace has 1387 (11.8%) zerosZeros

Reproduction

Analysis started2024-07-04 08:20:38.939047
Analysis finished2024-07-04 08:21:05.199237
Duration26.26 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

object_category
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
0
11393 
1
 
331

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11393
97.2%
1 331
 
2.8%

Length

2024-07-04T10:21:05.367069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:05.545228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 11393
97.2%
1 331
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 11393
97.2%
1 331
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11393
97.2%
1 331
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11393
97.2%
1 331
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11393
97.2%
1 331
 
2.8%

object_type
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7718356
Minimum0
Maximum19
Zeros9318
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:05.707398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.8990794
Coefficient of variation (CV)2.2005876
Kurtosis3.8368622
Mean1.7718356
Median Absolute Deviation (MAD)0
Skewness2.1794956
Sum20773
Variance15.20282
MonotonicityNot monotonic
2024-07-04T10:21:05.909301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 9318
79.5%
8 1126
 
9.6%
5 301
 
2.6%
14 241
 
2.1%
2 225
 
1.9%
11 178
 
1.5%
16 127
 
1.1%
9 52
 
0.4%
15 38
 
0.3%
12 32
 
0.3%
Other values (10) 86
 
0.7%
ValueCountFrequency (%)
0 9318
79.5%
1 7
 
0.1%
2 225
 
1.9%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 301
 
2.6%
6 22
 
0.2%
7 3
 
< 0.1%
8 1126
 
9.6%
9 52
 
0.4%
ValueCountFrequency (%)
19 20
 
0.2%
18 4
 
< 0.1%
17 20
 
0.2%
16 127
1.1%
15 38
 
0.3%
14 241
2.1%
13 1
 
< 0.1%
12 32
 
0.3%
11 178
1.5%
10 4
 
< 0.1%

price_display
Real number (ℝ)

Distinct1996
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2122.8848
Minimum1
Maximum15000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:06.135688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile990
Q11480
median1870
Q32500
95-th percentile3943.85
Maximum15000
Range14999
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation1087.9014
Coefficient of variation (CV)0.51246372
Kurtosis19.279909
Mean2122.8848
Median Absolute Deviation (MAD)470
Skewness3.1119699
Sum24888701
Variance1183529.5
MonotonicityNot monotonic
2024-07-04T10:21:06.381695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 116
 
1.0%
1600 112
 
1.0%
1450 110
 
0.9%
1650 109
 
0.9%
1400 107
 
0.9%
1800 105
 
0.9%
1950 102
 
0.9%
1700 99
 
0.8%
2000 96
 
0.8%
1550 96
 
0.8%
Other values (1986) 10672
91.0%
ValueCountFrequency (%)
1 2
< 0.1%
60 1
< 0.1%
150 2
< 0.1%
159 1
< 0.1%
165 1
< 0.1%
175 1
< 0.1%
240 1
< 0.1%
250 1
< 0.1%
310 1
< 0.1%
360 1
< 0.1%
ValueCountFrequency (%)
15000 3
< 0.1%
13900 1
 
< 0.1%
13500 1
 
< 0.1%
12653 1
 
< 0.1%
12500 1
 
< 0.1%
12000 3
< 0.1%
11900 1
 
< 0.1%
11800 1
 
< 0.1%
11000 2
< 0.1%
10900 2
< 0.1%

price_display_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
1
11722 
0
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11722
> 99.9%
0 2
 
< 0.1%

Length

2024-07-04T10:21:06.609805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:06.766972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 11722
> 99.9%
0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 11722
> 99.9%
0 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11722
> 99.9%
0 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11722
> 99.9%
0 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11722
> 99.9%
0 2
 
< 0.1%

price_unit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
0
11722 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11722
> 99.9%
1 2
 
< 0.1%

Length

2024-07-04T10:21:06.933462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:07.090192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 11722
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11722
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11722
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11722
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11722
> 99.9%
1 2
 
< 0.1%

number_of_rooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2624104
Minimum0
Maximum10.5
Zeros215
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:07.252123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.5
median3.5
Q34.5
95-th percentile5.5
Maximum10.5
Range10.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3229948
Coefficient of variation (CV)0.40552677
Kurtosis0.88407228
Mean3.2624104
Median Absolute Deviation (MAD)1
Skewness0.1563592
Sum38248.5
Variance1.7503151
MonotonicityNot monotonic
2024-07-04T10:21:07.455463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3.5 2943
25.1%
4.5 2078
17.7%
2.5 1956
16.7%
3 1003
 
8.6%
1 759
 
6.5%
2 708
 
6.0%
4 606
 
5.2%
1.5 552
 
4.7%
5.5 521
 
4.4%
0 215
 
1.8%
Other values (10) 383
 
3.3%
ValueCountFrequency (%)
0 215
 
1.8%
1 759
 
6.5%
1.5 552
 
4.7%
2 708
 
6.0%
2.5 1956
16.7%
3 1003
 
8.6%
3.5 2943
25.1%
4 606
 
5.2%
4.5 2078
17.7%
5 124
 
1.1%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10 7
 
0.1%
9 4
 
< 0.1%
8.5 12
 
0.1%
8 13
 
0.1%
7.5 36
 
0.3%
7 24
 
0.2%
6.5 106
 
0.9%
6 56
 
0.5%
5.5 521
4.4%

floor
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9900205
Minimum-5
Maximum31
Zeros2812
Zeros (%)24.0%
Negative81
Negative (%)0.7%
Memory size91.7 KiB
2024-07-04T10:21:07.654825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum31
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1596249
Coefficient of variation (CV)1.0852275
Kurtosis17.576672
Mean1.9900205
Median Absolute Deviation (MAD)1
Skewness2.880149
Sum23331
Variance4.6639796
MonotonicityNot monotonic
2024-07-04T10:21:07.870514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 2812
24.0%
1 2598
22.2%
2 2585
22.0%
3 1784
15.2%
4 892
 
7.6%
5 444
 
3.8%
6 198
 
1.7%
7 94
 
0.8%
8 58
 
0.5%
-1 48
 
0.4%
Other values (21) 211
 
1.8%
ValueCountFrequency (%)
-5 1
 
< 0.1%
-4 6
 
0.1%
-3 10
 
0.1%
-2 16
 
0.1%
-1 48
 
0.4%
0 2812
24.0%
1 2598
22.2%
2 2585
22.0%
3 1784
15.2%
4 892
 
7.6%
ValueCountFrequency (%)
31 1
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 3
 
< 0.1%
22 1
 
< 0.1%
21 2
 
< 0.1%
19 2
 
< 0.1%
18 7
0.1%
17 4
< 0.1%
16 8
0.1%

is_furnished
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
0.0
9790 
1.0
1934 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35172
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9790
83.5%
1.0 1934
 
16.5%

Length

2024-07-04T10:21:08.083449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:08.244152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9790
83.5%
1.0 1934
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 21514
61.2%
. 11724
33.3%
1 1934
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21514
61.2%
. 11724
33.3%
1 1934
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21514
61.2%
. 11724
33.3%
1 1934
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21514
61.2%
. 11724
33.3%
1 1934
 
5.5%

is_temporary
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
0.0
10874 
1.0
 
850

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35172
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10874
92.7%
1.0 850
 
7.3%

Length

2024-07-04T10:21:08.417033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:08.576550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10874
92.7%
1.0 850
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 22598
64.2%
. 11724
33.3%
1 850
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22598
64.2%
. 11724
33.3%
1 850
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22598
64.2%
. 11724
33.3%
1 850
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22598
64.2%
. 11724
33.3%
1 850
 
2.4%

is_selling_furniture
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
0.0
11399 
1.0
 
325

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35172
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11399
97.2%
1.0 325
 
2.8%

Length

2024-07-04T10:21:08.752209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:08.909644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11399
97.2%
1.0 325
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 23123
65.7%
. 11724
33.3%
1 325
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23123
65.7%
. 11724
33.3%
1 325
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23123
65.7%
. 11724
33.3%
1 325
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23123
65.7%
. 11724
33.3%
1 325
 
0.9%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct1555
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5372.6726
Minimum1000
Maximum9657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:09.494842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1201
Q13072
median4833
Q38052
95-th percentile9113
Maximum9657
Range8657
Interquartile range (IQR)4980

Descriptive statistics

Standard deviation2696.1554
Coefficient of variation (CV)0.5018276
Kurtosis-1.3885834
Mean5372.6726
Median Absolute Deviation (MAD)2533
Skewness-0.0032038067
Sum62989214
Variance7269254
MonotonicityNot monotonic
2024-07-04T10:21:09.732713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 229
 
2.0%
1700 223
 
1.9%
2300 144
 
1.2%
8050 144
 
1.2%
8004 126
 
1.1%
4052 125
 
1.1%
8003 110
 
0.9%
4123 110
 
0.9%
6900 106
 
0.9%
4058 100
 
0.9%
Other values (1545) 10307
87.9%
ValueCountFrequency (%)
1000 3
 
< 0.1%
1001 1
 
< 0.1%
1003 39
0.3%
1004 42
0.4%
1005 13
 
0.1%
1006 20
0.2%
1007 32
0.3%
1008 30
0.3%
1009 25
0.2%
1010 20
0.2%
ValueCountFrequency (%)
9657 3
 
< 0.1%
9656 4
< 0.1%
9650 4
< 0.1%
9643 2
 
< 0.1%
9642 1
 
< 0.1%
9630 9
0.1%
9620 3
 
< 0.1%
9615 1
 
< 0.1%
9607 1
 
< 0.1%
9606 7
0.1%

city
Real number (ℝ)

Distinct1813
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean923.06269
Minimum0
Maximum1812
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:09.965254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile96
Q1412
median892
Q31422
95-th percentile1746
Maximum1812
Range1812
Interquartile range (IQR)1010

Descriptive statistics

Standard deviation576.25604
Coefficient of variation (CV)0.624287
Kurtosis-1.3267527
Mean923.06269
Median Absolute Deviation (MAD)530
Skewness-0.035457575
Sum10821987
Variance332071.02
MonotonicityNot monotonic
2024-07-04T10:21:10.210718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1746 876
 
7.5%
110 653
 
5.6%
1735 362
 
3.1%
1422 350
 
3.0%
138 272
 
2.3%
827 224
 
1.9%
551 222
 
1.9%
789 142
 
1.2%
1680 114
 
1.0%
41 112
 
1.0%
Other values (1803) 8397
71.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 37
0.3%
5 12
 
0.1%
6 2
 
< 0.1%
7 20
0.2%
8 1
 
< 0.1%
9 13
 
0.1%
ValueCountFrequency (%)
1812 1
 
< 0.1%
1811 1
 
< 0.1%
1810 5
< 0.1%
1809 10
0.1%
1808 1
 
< 0.1%
1807 1
 
< 0.1%
1806 1
 
< 0.1%
1805 1
 
< 0.1%
1804 1
 
< 0.1%
1803 1
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct9639
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.147571
Minimum45.826182
Maximum47.768032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:10.455817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum45.826182
5-th percentile46.203546
Q146.950762
median47.318822
Q347.434064
95-th percentile47.563037
Maximum47.768032
Range1.94185
Interquartile range (IQR)0.4833025

Descriptive statistics

Standard deviation0.41363466
Coefficient of variation (CV)0.0087731914
Kurtosis0.70004736
Mean47.147571
Median Absolute Deviation (MAD)0.19344001
Skewness-1.2225721
Sum552758.12
Variance0.17109363
MonotonicityNot monotonic
2024-07-04T10:21:10.713835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.77822161 27
 
0.2%
46.0127616 23
 
0.2%
47.1131916 20
 
0.2%
46.0105316 13
 
0.1%
47.3675816 13
 
0.1%
47.55681161 12
 
0.1%
47.39155161 12
 
0.1%
47.37890161 12
 
0.1%
46.4625816 11
 
0.1%
47.3622505 11
 
0.1%
Other values (9629) 11570
98.7%
ValueCountFrequency (%)
45.8261816 1
< 0.1%
45.8310216 1
< 0.1%
45.8329516 2
< 0.1%
45.8332216 1
< 0.1%
45.83389161 1
< 0.1%
45.8357516 1
< 0.1%
45.8379816 1
< 0.1%
45.83800161 1
< 0.1%
45.83805161 2
< 0.1%
45.8417516 1
< 0.1%
ValueCountFrequency (%)
47.7680316 1
< 0.1%
47.7566216 1
< 0.1%
47.75052161 1
< 0.1%
47.75009161 1
< 0.1%
47.75003161 1
< 0.1%
47.7495116 1
< 0.1%
47.7469616 1
< 0.1%
47.74692161 1
< 0.1%
47.74690161 1
< 0.1%
47.74680161 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct9751
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9930144
Minimum5.9918812
Maximum10.364311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:10.973042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.9918812
5-th percentile6.6052572
Q17.4456337
median7.9095612
Q38.5733037
95-th percentile9.3834012
Maximum10.364311
Range4.37243
Interquartile range (IQR)1.12767

Descriptive statistics

Standard deviation0.84167478
Coefficient of variation (CV)0.1053013
Kurtosis-0.72970529
Mean7.9930144
Median Absolute Deviation (MAD)0.62267
Skewness-0.095036869
Sum93710.101
Variance0.70841644
MonotonicityNot monotonic
2024-07-04T10:21:11.215483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.156761234 27
 
0.2%
8.96301124 23
 
0.2%
7.301061235 20
 
0.2%
6.840561235 17
 
0.1%
8.521711235 13
 
0.1%
8.965131235 13
 
0.1%
8.509341238 12
 
0.1%
8.525991235 12
 
0.1%
7.587291239 12
 
0.1%
8.5222218 11
 
0.1%
Other values (9741) 11564
98.6%
ValueCountFrequency (%)
5.991881235 1
< 0.1%
6.019591235 1
< 0.1%
6.019631235 1
< 0.1%
6.036691235 1
< 0.1%
6.049101235 1
< 0.1%
6.078611235 1
< 0.1%
6.078891236 2
< 0.1%
6.079501235 1
< 0.1%
6.081931239 1
< 0.1%
6.087281235 1
< 0.1%
ValueCountFrequency (%)
10.36431123 1
< 0.1%
10.29604123 1
< 0.1%
10.09659123 1
< 0.1%
9.868861235 1
< 0.1%
9.833921236 1
< 0.1%
9.831621235 1
< 0.1%
9.824991235 1
< 0.1%
9.824821235 1
< 0.1%
9.816231235 1
< 0.1%
9.816201239 1
< 0.1%

year_built
Real number (ℝ)

ZEROS 

Distinct170
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.471739
Minimum-1
Maximum224
Zeros294
Zeros (%)2.5%
Negative18
Negative (%)0.2%
Memory size91.7 KiB
2024-07-04T10:21:11.451561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q139.479863
median39.479863
Q339.479863
95-th percentile74
Maximum224
Range225
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.461238
Coefficient of variation (CV)0.59438066
Kurtosis11.499572
Mean39.471739
Median Absolute Deviation (MAD)0
Skewness2.1750997
Sum462766.67
Variance550.4297
MonotonicityNot monotonic
2024-07-04T10:21:11.695306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.47986307 6820
58.2%
0 294
 
2.5%
1 157
 
1.3%
2 138
 
1.2%
6 128
 
1.1%
7 117
 
1.0%
3 113
 
1.0%
54 109
 
0.9%
9 109
 
0.9%
8 107
 
0.9%
Other values (160) 3632
31.0%
ValueCountFrequency (%)
-1 18
 
0.2%
0 294
2.5%
1 157
1.3%
2 138
1.2%
3 113
 
1.0%
4 92
 
0.8%
5 96
 
0.8%
6 128
1.1%
7 117
 
1.0%
8 107
 
0.9%
ValueCountFrequency (%)
224 12
0.1%
219 1
 
< 0.1%
217 1
 
< 0.1%
213 1
 
< 0.1%
212 1
 
< 0.1%
209 2
 
< 0.1%
206 1
 
< 0.1%
204 2
 
< 0.1%
199 1
 
< 0.1%
195 1
 
< 0.1%

year_renovated
Real number (ℝ)

ZEROS 

Distinct139
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.888156
Minimum-1
Maximum224
Zeros757
Zeros (%)6.5%
Negative25
Negative (%)0.2%
Memory size91.7 KiB
2024-07-04T10:21:11.934243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q19
median39.479863
Q339.479863
95-th percentile57
Maximum224
Range225
Interquartile range (IQR)30.479863

Descriptive statistics

Standard deviation20.711948
Coefficient of variation (CV)0.6929818
Kurtosis5.1853193
Mean29.888156
Median Absolute Deviation (MAD)0
Skewness0.95508546
Sum350408.74
Variance428.9848
MonotonicityNot monotonic
2024-07-04T10:21:12.170688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.47986307 5972
50.9%
0 757
 
6.5%
1 424
 
3.6%
2 327
 
2.8%
3 242
 
2.1%
4 227
 
1.9%
7 225
 
1.9%
6 222
 
1.9%
5 198
 
1.7%
9 195
 
1.7%
Other values (129) 2935
25.0%
ValueCountFrequency (%)
-1 25
 
0.2%
0 757
6.5%
1 424
3.6%
2 327
2.8%
3 242
 
2.1%
4 227
 
1.9%
5 198
 
1.7%
6 222
 
1.9%
7 225
 
1.9%
8 161
 
1.4%
ValueCountFrequency (%)
224 2
< 0.1%
188 1
 
< 0.1%
174 3
< 0.1%
172 1
 
< 0.1%
166 1
 
< 0.1%
163 3
< 0.1%
162 2
< 0.1%
161 3
< 0.1%
154 1
 
< 0.1%
144 1
 
< 0.1%

moving_date_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
1
5510 
2
3206 
0
3008 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11724
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5510
47.0%
2 3206
27.3%
0 3008
25.7%

Length

2024-07-04T10:21:12.383619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:12.553747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 5510
47.0%
2 3206
27.3%
0 3008
25.7%

Most occurring characters

ValueCountFrequency (%)
1 5510
47.0%
2 3206
27.3%
0 3008
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5510
47.0%
2 3206
27.3%
0 3008
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5510
47.0%
2 3206
27.3%
0 3008
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5510
47.0%
2 3206
27.3%
0 3008
25.7%

reserved
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.7 KiB
0.0
11717 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35172
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11717
99.9%
1.0 7
 
0.1%

Length

2024-07-04T10:21:12.737308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:21:12.909260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11717
99.9%
1.0 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 23441
66.6%
. 11724
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23441
66.6%
. 11724
33.3%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23441
66.6%
. 11724
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23441
66.6%
. 11724
33.3%
1 7
 
< 0.1%

livingspace
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct254
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.392613
Minimum0
Maximum1275
Zeros1387
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size91.7 KiB
2024-07-04T10:21:13.099836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147
median74
Q397
95-th percentile148
Maximum1275
Range1275
Interquartile range (IQR)50

Descriptive statistics

Standard deviation48.428886
Coefficient of variation (CV)0.65986049
Kurtosis49.614928
Mean73.392613
Median Absolute Deviation (MAD)25
Skewness2.9830714
Sum860455
Variance2345.357
MonotonicityNot monotonic
2024-07-04T10:21:13.352911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1387
 
11.8%
70 326
 
2.8%
80 292
 
2.5%
100 247
 
2.1%
90 244
 
2.1%
75 243
 
2.1%
65 221
 
1.9%
60 221
 
1.9%
85 213
 
1.8%
120 173
 
1.5%
Other values (244) 8157
69.6%
ValueCountFrequency (%)
0 1387
11.8%
1 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 7
 
0.1%
11 6
 
0.1%
12 18
 
0.2%
13 9
 
0.1%
14 17
 
0.1%
15 31
 
0.3%
ValueCountFrequency (%)
1275 1
< 0.1%
902 1
< 0.1%
708 1
< 0.1%
705 1
< 0.1%
700 1
< 0.1%
550 2
< 0.1%
425 1
< 0.1%
373 1
< 0.1%
350 2
< 0.1%
349 1
< 0.1%

Interactions

2024-07-04T10:21:02.385525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:40.859701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:42.982856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:45.528344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:47.546167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:49.652797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:51.614601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:53.700129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:55.820588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:58.153474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:00.421029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:02.580573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:41.058760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:43.187042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:45.719077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:47.744803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:49.842609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:51.816161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:53.905855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:56.008075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:58.342199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:00.626037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:02.772200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:41.256098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:43.389306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:45.912542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:47.948457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:50.028126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:52.016721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:54.106280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:56.193835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:58.525623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:00.817137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:02.954870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:41.441947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:43.952221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:46.084031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:48.127424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:50.199241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:52.213472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:54.288672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:56.368052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:58.699803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:00.989001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:03.140347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:41.639645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:44.153881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:46.282769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:48.345522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:50.385983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:52.405077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:54.486633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:56.550881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:58.901375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:01.176927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:03.316216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:41.818484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:44.330718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:46.455717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:48.534526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:50.554312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:52.582069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:54.670056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:56.716664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:59.113991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:01.342292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:03.506205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:42.020269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:44.533776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:46.639552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:48.726530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:50.745499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:52.771245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:54.867281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:57.278635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:59.303463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:01.525788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:03.720716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:42.235712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:44.748467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:46.842357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:48.933098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:50.950149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:52.980032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:55.070250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:57.469558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:59.499177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:01.721482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:03.890148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:42.417786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:44.925497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:47.017094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:49.106629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:51.111280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:53.153569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:55.258816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:57.637683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:59.740359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:01.885936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:04.060523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:42.599409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:45.115162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:47.188448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:49.280795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:51.268330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:53.337832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:55.452648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:57.805460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:59.932534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:02.045485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:04.231503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:42.786527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:45.310343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:47.361211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:49.461239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:51.438021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:53.515014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:55.629386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:57.978015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:00.137566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:21:02.203830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-04T10:21:13.549623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
cityflooris_furnishedis_selling_furnitureis_temporarylatitudelivingspacelongitudemoving_date_typenumber_of_roomsobject_categoryobject_typeprice_displayprice_display_typeprice_unitreservedyear_builtyear_renovatedzipcode
city1.000-0.0540.2210.0540.1800.009-0.0620.2890.065-0.0790.0580.0340.1400.0000.0000.000-0.020-0.0350.309
floor-0.0541.0000.0470.0380.0530.0090.018-0.0740.039-0.0040.112-0.055-0.0270.0000.0000.0000.0470.050-0.083
is_furnished0.2210.0471.0000.0000.5380.010-0.1640.1390.143-0.2510.0350.4300.1990.0000.0000.000-0.0150.0700.125
is_selling_furniture0.0540.0380.0001.0000.0000.002-0.0390.0310.041-0.0380.000-0.066-0.0050.0000.0000.000-0.039-0.0310.035
is_temporary0.1800.0530.5380.0001.0000.032-0.0130.0910.139-0.0510.0120.0980.0870.0000.0000.000-0.0110.0570.107
latitude0.0090.0090.0100.0020.0321.0000.0090.4230.112-0.0220.0640.0130.0130.0000.0000.0000.000-0.0760.515
livingspace-0.0620.018-0.164-0.039-0.0130.0091.000-0.0060.0600.6690.3330.0410.4830.0000.0000.000-0.121-0.1650.008
longitude0.289-0.0740.1390.0310.0910.423-0.0061.0000.111-0.0290.0760.0850.0560.0000.0000.000-0.043-0.1320.952
moving_date_type0.0650.0390.1430.0410.1390.1120.0600.1111.000-0.0010.099-0.039-0.0930.0000.0000.0150.0140.045-0.031
number_of_rooms-0.079-0.004-0.251-0.038-0.051-0.0220.669-0.029-0.0011.0000.473-0.0490.3500.0000.0000.000-0.054-0.097-0.009
object_category0.0580.1120.0350.0000.0120.0640.3330.0760.0990.4731.0000.3860.1760.0000.0000.000-0.002-0.0740.012
object_type0.034-0.0550.430-0.0660.0980.0130.0410.085-0.039-0.0490.3861.0000.2940.0000.0000.000-0.0260.0130.076
price_display0.140-0.0270.199-0.0050.0870.0130.4830.056-0.0930.3500.1760.2941.0000.0000.0000.027-0.178-0.1410.084
price_display_type0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.7500.000-0.001-0.007-0.005
price_unit0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7501.0000.0000.0010.0070.005
reserved0.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0270.0000.0001.000-0.018-0.030-0.005
year_built-0.0200.047-0.015-0.039-0.0110.000-0.121-0.0430.014-0.054-0.002-0.026-0.178-0.0010.001-0.0181.0000.462-0.057
year_renovated-0.0350.0500.070-0.0310.057-0.076-0.165-0.1320.045-0.097-0.0740.013-0.141-0.0070.007-0.0300.4621.000-0.139
zipcode0.309-0.0830.1250.0350.1070.5150.0080.952-0.031-0.0090.0120.0760.084-0.0050.005-0.005-0.057-0.1391.000

Missing values

2024-07-04T10:21:04.535674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-04T10:21:04.998272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace
0015610.0101.00.01.00.00.09008142247.4424309.39251039.47986339.47986310.017.0
1002370.0102.50.00.00.00.02000100646.7988036.85501020.00000020.00000020.0145.0
2003104.0100.00.00.00.00.08001173547.3698268.53179739.47986339.47986300.024.0
3004246.0101.00.00.00.00.08001173547.3698268.53179739.47986339.47986300.036.0
4005076.0102.00.00.00.00.08001173547.3698268.53179739.47986339.47986300.046.0
5004453.0100.00.00.00.00.08001173547.3698268.53179739.47986339.47986300.040.0
6003104.0100.00.00.00.00.08001173547.3698268.53179739.47986339.47986300.023.0
7003415.0100.00.00.00.00.08001173547.3698268.53179739.47986339.47986300.030.0
8004142.0100.00.00.00.00.08002173547.3526518.53007139.47986339.47986300.040.0
9005273.0100.00.00.00.00.08002173547.3526518.53007139.47986339.47986300.066.0
object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace
11714001750.0102.52.01.00.00.05612159247.3449028.24639139.4798630.00000020.085.0
1171500600.0103.54.00.00.00.09000142247.4218829.37559139.4798636.00000020.065.0
11716001900.0101.52.00.00.00.08047174847.3852028.4938319.0000009.00000010.040.0
11717082750.0104.04.00.00.00.0405711047.5709027.5910110.0000000.00000010.0100.0
11718001200.0102.00.00.00.00.0471010747.3131927.69940139.47986339.47986300.060.0
11719082510.0103.03.01.00.00.0405411047.5463427.56284170.00000070.00000010.078.0
11720003270.0103.52.01.00.00.08048173547.3848728.4936516.0000006.00000010.0102.0
11721002500.0104.01.00.00.00.0830276447.4448728.58114164.0000001.00000010.074.0
11722001125.0101.01.00.00.00.0306518446.9736627.4893310.0000000.00000000.027.0
11723002050.0103.52.00.00.00.0895340747.4058128.40345139.47986339.47986310.092.0

Duplicate rows

Most frequently occurring

object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace# duplicates
14001540.0104.53.00.00.00.09014142247.4082829.33238139.47986339.47986300.095.03
39081850.0101.50.01.00.00.0550270747.3872928.12038139.47986339.47986320.050.03
53016650.0102.01.00.00.00.09000142247.4233129.36804139.47986339.47986310.011.03
000890.0101.51.00.00.00.09320143847.5069929.41057139.47986339.47986310.030.02
100990.0101.04.00.00.00.0405211047.5549227.60828139.4798639.00000020.012.02
2001040.0103.01.00.00.00.0230078947.0932926.80698139.47986339.47986320.058.02
3001090.0101.50.00.00.00.02557146347.1131927.30106139.47986339.47986310.048.02
4001120.0102.02.00.00.00.09400124747.4778429.48692139.4798631.00000000.034.02
5001350.0102.51.00.00.00.02557146347.1131927.30106139.47986339.47986310.062.02
6001350.0103.02.00.00.00.0920060847.4200029.24294149.00000049.00000010.066.02